Spencer, AV, Cox, A and Walters, K (2014) Comparing the efficacy of SNP filtering methods for identifying a single causal SNP in a known association region. Annals of Human Genetics, 78 (1). 50 - 61. ISSN 0003-4800
Abstract
Genome-wide association studies have successfully identified associations between common diseases and a large number of single nucleotide polymorphisms (SNPs) across the genome. We investigate the effectiveness of several statistics, including p-values, likelihoods, genetic map distance and linkage disequilibrium between SNPs, in filtering SNPs in several disease-associated regions. We use simulated data to compare the efficacy of filters with different sample sizes and for causal SNPs with different minor allele frequencies (MAFs) and effect sizes, focusing on the small effect sizes and MAFs likely to represent the majority of unidentified causal SNPs. In our analyses, of all the methods investigated, filtering on the ranked likelihoods consistently retains the true causal SNP with the highest probability for a given false positive rate. This was the case for all the local linkage disequilibrium patterns investigated. Our results indicate that when using this method to retain only the top 5% of SNPs, even a causal SNP with an odds ratio of 1.1 and MAF of 0.08 can be retained with a probability exceeding 0.9 using an overall sample size of 50,000. © 2013 John Wiley & Sons Ltd/University College London.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Fine-mapping, likelihood, single nucleotide polymorphism, complex disease, causal variants, LD, p-value |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Oncology (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number EPSRC X/004943 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Apr 2014 12:46 |
Last Modified: | 07 Jun 2024 14:31 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1111/ahg.12043 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:78643 |